Yikai Wu
Papers
1
Total Citations
3
H-Index
1
About
Yikai Wu is a researcher whose work sits at the intersection of computer vision and power infrastructure maintenance, with a particular focus on applying deep learning to insulator instance segmentation. Their most cited paper, "Insulator instance segmentation based on deep learning network Mask RCNN" (2022, 3 citations), addresses a critical challenge in modern power grid management: the automated detection and segmentation of insulators from images. As power supply line construction expands and stable transmission becomes essential for daily life, large-scale grid maintenance has emerged as a pressing problem for utility enterprises. Wu’s contribution lies in adapting the Mask R-CNN architecture—a state-of-the-art instance segmentation model—to the specific domain of insulator detection, enabling more precise and efficient identification of these critical components. This work represents an important step toward automating power line inspections, reducing the need for manual labor and improving the reliability of electrical infrastructure. While early in their citation trajectory, Wu’s research signals a growing intersection between deep learning and industrial maintenance, offering practical solutions for the energy sector’s evolving technological needs.
Research Focus
Key Achievements
Top Papers
- 1Insulator instance segmentation based on deep learning network Mask RCNN3 citations · 2022